Dynamical mass inference of galaxy clusters with neural flows

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt


  • staa2886

    Forlagets udgivne version, 1,5 MB, PDF-dokument

We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, that is, the observed line-of-sight velocities and projected distances of galaxies from the cluster centre. Our method employs normalizing flows, a deep neural network capable of learning arbitrary high-dimensional probability distributions, and inherently accounts, to an adequate extent, for the presence of interloper galaxies which are not bounded to a given cluster, the primary contaminant of dynamical mass measurements. We validate and showcase the performance of our neural flow approach to robustly infer the dynamical mass of clusters from a realistic mock cluster catalogue. A key aspect of our novel algorithm is that it yields the probability density function of the mass of a particular cluster, thereby providing a principled way of quantifying uncertainties, in contrast to conventional machine learning (ML) approaches. The neural network mass predictions, when applied to a contaminated catalogue with interlopers, have a mean overall logarithmic residual scatter of 0.028 dex, with a lognormal scatter of 0.126 dex, which goes down to 0.089 dex for clusters in the intermediate- to high-mass range. This is an improvement by nearly a factor of 4 relative to the classical cluster mass scaling relation with the velocity dispersion, and outperforms recently proposed ML approaches. We also apply our neural flow mass estimator to a compilation of galaxy observations of some well-studied clusters with robust dynamical mass estimates, further substantiating the efficacy of our algorithm.

TidsskriftMonthly Notices of the Royal Astronomical Society
Udgave nummer2
Sider (fra-til)1985-1997
Antal sider13
StatusUdgivet - 21 sep. 2020


Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk

Ingen data tilgængelig

ID: 252292095